The present disclosure relates generally to magnetic resonance fingerprinting (MRF), and in particular to increase quality of iterative MRF and to decrease reconstruction time for iterative MRF.
Magnetic resonance fingerprinting is a technique for multi-parametric quantitative imaging. The technique aims to obtain multiple parameters, such as spin-lattice relaxation time (T1) (also known as the longitudinal relaxation time), spin-spin transverse relaxation time (T2) (also known as the transverse relaxation time), proton density (PD), and the like, for a test object by applying a series of excitations to a test object, acquiring a signal response of the object to the series of excitations, and matching the undersampled signal response to a simulated response found in a dictionary or database of possible simulated responses.
Each simulated response stored in the dictionary is generated by running Bloch equations with relevant values for magnetic resonance parameters (T1, T2, PD, and the like). Once a match is found between the undersampled signal response and a simulated response in the dictionary, the magnetic resonance parameters (T1, T2, PD, and the like) corresponding to the matched simulated response can be retrieved from the dictionary and used for further imaging purposes. For a single iteration, the amount of data in an MR signal is not enough to provide a match to the dictionary entry with sufficient accuracy. Iterative processes are therefore employed to help refine the dictionary matching process. However, iterative processes require the repetition of various computationally expensive steps, such as dictionary search steps and signal comparison steps.